1. Field of the Invention
The present invention pertains to a neural network used for matching fluorescence "fingerprints" of unknown hydrocarbon mixtures with a library of known and previously identified "fingerprints".
2. The Prior Art
A technique for developing a "fingerprint" of an unknown hydrocarbon mixture by fluorescence scanning has become an important and reliable tool for identifying complex mixtures of aromatic hydrocarbons, such as crude oils and refined petroleum products. Such fingerprinting can be an efficient technique for identifying, characterizing and classifying the source of unknown hydrocarbons. The fingerprint is obtained by exciting a sample with varying wavelengths of light in the UV region and measuring the intensity of the resulting fluorescence emissions. A total scan results in a unique three-dimensional fluorescence spectra.
Spectral features of different types of polyaromatic compounds and mixtures have been found to correlate very well with their respective physical and chemical properties. Spectral fingerprints have helped in characterizing unknown fluorescent substances and to differentiate them from crude oils. They have also been used to relate a sample to its source.
As the variety of samples examined has increased, the interpretation of fingerprints has become complicated. Visual comparison of the normal emission scans has been aided by the processes of spectral subtraction and by taking higher order derivatives. However, these processes of differentiation are limited to the small number of known spectra that the human memory can hold. Whereas it is possible to increase the number of correlations, the process becomes tedious and time-consuming. Even then, the accuracy is not all it can be.
Recognition of spectral patterns is more accurately interpreted by manipulation of 3D-data. Several variables have been found to contribute to the accuracy of the spectral interpretation in the course of the development of total scanning fluorescence. What complicates the interpretation of spectra is the discovery of non-linear relations for some variables. Each variable measurement may not be significant by itself, and may contribute differently to the identification or classification of an oil.
There are other data that have been found to increase the accuracy and, at the same time, the ambiguity of predicting properties from spectral fingerprints beyond visual examination of spectra. Calculations of fluorescence yields of the normalized solutions, emission intensity ratios, spectral wavelength ranges, and correlations with other data analyses, such a SARA fractionation and geochemical analyses have contributed significantly to the process of matching fingerprints. SARA stands for Saturates, Aromatics, Resins and Asphaltenes--the four main fractions of classes of compounds that make up the chemical composition of crude oil. SARA Analysis is a chemical process that separates these fractions stepwise by using different organic solvents to isolate each fraction. The analytical technique is called HPLC, which stands for High Pressure Liquid Chromatography.
With the increase in the variables, the growing variety of fluorescent substances to be compared, and the limited capacity of human memory, a method of improving the pattern recognition and interpretation significantly is essential. The application of a neural network system for the existing collection of TSF spectra and information seems to be very promising. The software for the present invention is notable for its resourceful use of assumption-free variables for increased speed of analysis, accuracy and discreetness of data recognition, decreased learning curve, and subjectivity for the analyst.